Parametric optimization in drilling of sisal–glass reinforced epoxy composites using Taguchi grey relational analysis method
Bibliographic record
Abstract
This research work intends to study the effect of hybridization of glass and sisal fiber, stacking sequence and tensile properties of the composite. The sisal-glass fiber hybrid composites laminates are prepared using reinforced plain woven sisal fabric (unidirectional) and plainwoven glass fabric. In this research study, 27 experiments are conducted as per L27 orthogonal array. Five process parameters are selected and three responses are considered in this work. The drilling of the composite specimen is considered and the drilling process parameters such as speed, feed rate, drill diameter, material thickness, and drill point angle are selected. The responses considered in this work are delamination factor, thrust force, and torque. Taguchi analysis is performed and the response table for means for the responses is determined, and the most influencing parameter in the drilling of the composite specimen is analyzed. The grey relational coefficients are computed and followed with the computation of the grey relational grade. The grey relational grades are calculated for determining the highest contributing parameter in the drilling of the sisal fiber and glass fiber reinforced hybrid composite specimen. The optimum drilling process parameters are ranked and the ranks presented represent the sequence of run resulting in optimum solutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".